MTCNN.py 文件源码

def create_Kao_Onet( weight_path = 'model48.h5'):
    input = Input(shape = [48,48,3])
    x = Conv2D(32, (3, 3), strides=1, padding='valid', name='conv1')(input)
    x = PReLU(shared_axes=[1,2],name='prelu1')(x)
    x = MaxPool2D(pool_size=3, strides=2, padding='same')(x)
    x = Conv2D(64, (3, 3), strides=1, padding='valid', name='conv2')(x)
    x = PReLU(shared_axes=[1,2],name='prelu2')(x)
    x = MaxPool2D(pool_size=3, strides=2)(x)
    x = Conv2D(64, (3, 3), strides=1, padding='valid', name='conv3')(x)
    x = PReLU(shared_axes=[1,2],name='prelu3')(x)
    x = MaxPool2D(pool_size=2)(x)
    x = Conv2D(128, (2, 2), strides=1, padding='valid', name='conv4')(x)
    x = PReLU(shared_axes=[1,2],name='prelu4')(x)
    x = Permute((3,2,1))(x)
    x = Flatten()(x)
    x = Dense(256, name='conv5') (x)
    x = PReLU(name='prelu5')(x)

    classifier = Dense(2, activation='softmax',name='conv6-1')(x)
    bbox_regress = Dense(4,name='conv6-2')(x)
    landmark_regress = Dense(10,name='conv6-3')(x)
    model = Model([input], [classifier, bbox_regress, landmark_regress])
    model.load_weights(weight_path, by_name=True)

    return model